Difference between revisions of "MSc: Computer Vision"

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= Computer Vision =
  
= Computer Vision =
  +
* '''Course name''': Computer Vision
  +
* '''Code discipline''': R-03
  +
* '''Subject area''':
  +
  +
== Short Description ==
  +
This course covers the following concepts: Computer vision techniques; Classical and deep learning models.
  +
  +
== Prerequisites ==
  +
  +
=== Prerequisite subjects ===
  +
  +
  +
=== Prerequisite topics ===
  +
  +
  +
== Course Topics ==
  +
{| class="wikitable"
  +
|+ Course Sections and Topics
  +
|-
  +
! Section !! Topics within the section
  +
|-
  +
| Image Acquisition and Basic Image Processing ||
  +
# Computer vision in action
  +
# The Human Vision System
  +
# Optical Illusions
  +
# Sampling and Quantization
  +
# Image Representation
  +
# Colour Spaces
  +
|-
  +
| Image Filtering and Binary Vision ||
  +
# Image noise
  +
# Convolutions and kernels
  +
# Smoothing and blurring
  +
# Thresholding and histograms
  +
# Morphological operations
  +
# Gradients and Edge detection
  +
|-
  +
| Feature Extractors and Descriptors ||
  +
# Histogram of Gradients (HoG)
  +
# Scale-invariant feature transform (SIFT)
  +
# Harris corner detector
  +
# Template matching
  +
# Bag of visual words
  +
# Face Detection and Recognition (Viola Johns)
  +
|-
  +
| Deep learning models for computer vision ||
  +
# You Only Look Once: Unified, Real-Time Object Detection (YOLO)
  +
# Generative Adversarial Networks (GAN)
  +
# Fully Convolutional Networks (FCN) for semantic segmentation
  +
# Multi Domain Network (MDNet) for object tracking
  +
# Generic Object Tracking Using Regression Networks (GOTURN) for object tracking
  +
|}
  +
== Intended Learning Outcomes (ILOs) ==
   
  +
=== What is the main purpose of this course? ===
−
* <span>'''Course name:'''</span> Computer Vision
  
  +
This course provides an intensive treatment of a cross-section of the key elements of computer vision, with an emphasis on implementing them in modern programming environments, and using them to solve real-world problems. The course will begin with the fundamentals of image processing and image filtering, but will quickly build to cover more advanced topics, including image segmentation, object detection and recognition, face detection, content-based image retrieval, artificial neural networks, convolutional neural networks, generative adversarial networks and much more. A key focus of the course is on providing students with not only theory but also hands-on practice of building their computer vision applications.
−
* <span>'''Course number:'''</span> R-03
  
−
* <span>'''Area of instruction:'''</span> Computer Science and Engineering
  
   
  +
=== ILOs defined at three levels ===
−
== Administrative details ==
  
   
  +
==== Level 1: What concepts should a student know/remember/explain? ====
−
* <span>'''Faculty:'''</span> Computer Science and Engineering
  
  +
By the end of the course, the students should be able to ...
−
* <span>'''Year of instruction:'''</span> 1st year of MSc
  
  +
* Robots visual perception strategies
−
* <span>'''Semester of instruction:'''</span> 2nd semester
  
  +
* Significant exposure to real-world implementations
−
* <span>'''No. of Credits:'''</span> 5 ECTS
  
  +
* To develop research interest in the theory and application of computer vision
−
* <span>'''Total workload on average:'''</span> 180 hours overall
  
−
* <span>'''Frontal lecture hours:'''</span> 2 hours per week.
  
−
* <span>'''Frontal tutorial hours:'''</span> 0 hours per week.
  
−
* <span>'''Lab hours:'''</span> 2 hours per week.
  
−
* <span>'''Individual lab hours:'''</span> 2 hours per week.
  
−
* <span>'''Frequency:'''</span> weekly throughout the semester.
  
−
* <span>'''Grading mode:'''</span> letters: A, B, C, D.
  
   
  +
==== Level 2: What basic practical skills should a student be able to perform? ====
−
== Course outline ==
  
  +
By the end of the course, the students should be able to ...
  +
* Suitability of different computer vision models in different scenarios
  +
* Ability to choose the right model for the given task
   
  +
==== Level 3: What complex comprehensive skills should a student be able to apply in real-life scenarios? ====
−
This course provides an intensive treatment of a cross-section of the key elements of computer vision, with an emphasis on implementing them in modern programming environments, and using them to solve real-world problems. The course will begin with the fundamentals of image processing and image filtering, but will quickly build to cover more advanced topics, including image segmentation, object detection and recognition, face detection, content-based image retrieval, artificial neural networks, convolutional neural networks, generative adversarial networks, image creation using GANs, and much more. A key focus of the course is on providing students with not only theory but also hands-on practice of building their computer vision applications.
  
  +
By the end of the course, the students should be able to ...
  +
* Hands on experience to implement different models to know inside behavior
  +
* Sufficient exposure to train and deploy model for the given task
  +
* Fine tune the deployed model in the real-world settings
  +
== Grading ==
   
−
== Expected learning outcomes ==
 +
=== Course grading range ===
  +
{| class="wikitable"
  +
|+
  +
|-
  +
! Grade !! Range !! Description of performance
  +
|-
  +
| A. Excellent || 90-100 || -
  +
|-
  +
| B. Good || 75-89 || -
  +
|-
  +
| C. Satisfactory || 60-74 || -
  +
|-
  +
| D. Poor || 0-59 || -
  +
|}
   
  +
=== Course activities and grading breakdown ===
−
* Apply knowledge of image acquisition, image processing, and image analysis to extract useful information from visual images.
  
  +
{| class="wikitable"
−
* Design, implement, and document appropriate, effective, and efficient software solutions for a variety of real-world computer vision problems.
  
  +
|+
−
* Exploit standard computer vision software libraries in the development of these solutions.
  
  +
|-
  +
! Activity Type !! Percentage of the overall course grade
  +
|-
  +
| Labs/seminar classes || 20
  +
|-
  +
| Interim performance assessment || 50
  +
|-
  +
| Exams || 30
  +
|}
   
  +
=== Recommendations for students on how to succeed in the course ===
−
== Required background knowledge ==
  
   
−
Solid knowledge of essential topics in the courses that are mentioned below.
  
   
  +
== Resources, literature and reference materials ==
−
== Prerequisite courses ==
  
   
  +
=== Open access resources ===
−
Linear Algebra, Calculus, Probability &amp; Statistics, Computer Programming, Basic Machine Learning.
  
  +
* Handouts supplied by the instructor
  +
* Materials from the interment and research papers shared by instructor
   
  +
=== Closed access resources ===
−
== Detailed topics covered in the course ==
  
   
−
* Introduction to Computer Vision, Image Acquisition, Basic Image Processing
  
−
* Kernels, Morphological operations, Smoothing and Blurring, Lightening and Color spaces
  
−
* Gradients, Edge Detection, Contours, Histograms, Labelling Connected Components
  
−
* Object Detection, Template Matching, Image Pyramids, Object Detection using HOG and SVM
  
−
* Image Classification, Common Machine Learning Algorithms for Image Classification
  
−
* Clustering, Bag of Visual Words, Image Pyramids, Image Classification Examples
  
−
* Face Detection
  
−
* Image Descriptors: Color channel statistics, Moments, Texture, HoGs
  
−
* Local features, Key point detectors, Local invariant descriptors, Binary Descriptors
  
−
* Video Processing: Moving object detection, background models
  
−
* Artificial Neural Nets and Convolutional Neural Nets
  
−
* Generative Adversarial Networks
  
−
* Case Studies: Research paper implementation and presentation
  
   
  +
=== Software and tools used within the course ===
−
== Textbook ==
  
  +
  +
= Teaching Methodology: Methods, techniques, & activities =
   
  +
== Activities and Teaching Methods ==
−
There is no specific text book for this course.
  
  +
{| class="wikitable"
  +
|+ Activities within each section
  +
|-
  +
! Learning Activities !! Section 1 !! Section 2 !! Section 3 !! Section 4
  +
|-
  +
| Development of individual parts of software product code || 1 || 1 || 1 || 1
  +
|-
  +
| Homework and group projects || 1 || 1 || 1 || 1
  +
|-
  +
| Midterm evaluation || 1 || 1 || 1 || 1
  +
|-
  +
| Testing (written or computer based) || 1 || 1 || 1 || 1
  +
|-
  +
| Discussions || 1 || 1 || 1 || 1
  +
|}
  +
== Formative Assessment and Course Activities ==
   
  +
=== Ongoing performance assessment ===
−
== Reference material ==
  
   
  +
==== Section 1 ====
−
*
 
  +
{| class="wikitable"
−
*
 
−
*
+
|+
  +
|-
−
*
 
  +
! Activity Type !! Content !! Is Graded?
  +
|-
  +
| Question || What are the color spaces and where it’s used? || 1
  +
|-
  +
| Question || What are the primary and secondary colors? || 1
  +
|-
  +
| Question || How image is formed into computers? || 1
  +
|-
  +
| Question || How you will convert the RGB to grayscale images || 1
  +
|-
  +
| Question || Loading and plotting the images in python environment || 0
  +
|-
  +
| Question || Convertion of different color spaces || 0
  +
|-
  +
| Question || How you find the skin in the images based on the color space models || 0
  +
|-
  +
| Question || how to find red eye dot in face using color space models || 0
  +
|}
  +
==== Section 2 ====
  +
{| class="wikitable"
  +
|+
  +
|-
  +
! Activity Type !! Content !! Is Graded?
  +
|-
  +
| Question || What are the challenges to perform histogram task? || 1
  +
|-
  +
| Question || Apply convolutional filter to calculate the response. || 1
  +
|-
  +
| Question || What kind of parameters are required to apply different image filters? || 1
  +
|-
  +
| Question || How you will compute the gradients of the image and its benefits? || 1
  +
|-
  +
| Question || Implement Otsu Method || 0
  +
|-
  +
| Question || Implement Sobel, Preweitt filters || 0
  +
|-
  +
| Question || Implement Canny edge detector || 0
  +
|-
  +
| Question || Perform analysis over the different filtering on the given images || 0
  +
|}
  +
==== Section 3 ====
  +
{| class="wikitable"
  +
|+
  +
|-
  +
! Activity Type !! Content !! Is Graded?
  +
|-
  +
| Question || How feature extractor works over the given image? || 1
  +
|-
  +
| Question || What is the difference between the feature extraction and descriptors? || 1
  +
|-
  +
| Question || Explain the examples of descriptors and feature extractors. || 1
  +
|-
  +
| Question || Write down the pros and cons of SIFT, HOG and Harris. || 1
  +
|-
  +
| Question || Implement template matching algorithm || 0
  +
|-
  +
| Question || Implement histogram of gradient using CV2 library || 0
  +
|-
  +
| Question || Implement of SIFT for the given task || 0
  +
|-
  +
| Question || Implement Harris corner detection || 0
  +
|-
  +
| Question || Analysis of different extractors for the given task || 0
  +
|}
  +
==== Section 4 ====
  +
{| class="wikitable"
  +
|+
  +
|-
  +
! Activity Type !! Content !! Is Graded?
  +
|-
  +
| Question || How classification task is different from detection task? || 1
  +
|-
  +
| Question || Explain the transfer learning mechanism for object detection task. || 1
  +
|-
  +
| Question || How many types of model exist for object tracking in videos. || 1
  +
|-
  +
| Question || Write down the pros and cons of YOLO, FCN and MDNet. || 1
  +
|-
  +
| Question || Implement YOLO using transfer learning mechanism || 0
  +
|-
  +
| Question || Implement GAN for MNIST dataset || 0
  +
|-
  +
| Question || Implement FCN and GOTURN || 0
  +
|-
  +
| Question || Analysis of different models for the given task || 0
  +
|}
  +
=== Final assessment ===
  +
'''Section 1'''
  +
# How you can distinguish different color spaces?
  +
# Explain and provide the reason for the blind spot creation in human eye.
  +
# In what scenarios computer vision is better than human vision?
  +
# Write down different robotic application areas where computer vision is applied successfully.
  +
'''Section 2'''
  +
# Calculate the kernels for the given images
  +
# Explain the difference between different filters
  +
# What is image noise and how it contributes to make the computer vision task difficult?
  +
# Apply different combination of the filters to achieve the required output of the given image.
  +
'''Section 3'''
  +
# How you distinguish different feature extractors and descriptors?
  +
# What are the possible methods to detect the corners?
  +
# How corners are useful to help the robotic vision task?
  +
# How you will patch the different images to construct the map of the location?
  +
'''Section 4'''
  +
# What are the loss functions used in YOLO?
  +
# What are the learnable parameters of FCN for semantic segmentation?
  +
# How semantic segmentation is different from instance segmentation?
  +
# Write the application areas for object tracking in robotics.
   
−
== Required computer resources ==
 +
=== The retake exam ===
  +
'''Section 1'''
   
  +
'''Section 2'''
−
Students should have laptops. A Mac or Window’s PC capable of running a scientific python development environment.
  
   
  +
'''Section 3'''
−
== Evaluation ==
  
   
  +
'''Section 4'''
−
* Quizzes (40%)
  
−
* Mid-term exam (15%)
  
−
* Case Study (15%)
  
−
* Final exam (30%)
  

Latest revision as of 11:43, 29 August 2022

Computer Vision

  • Course name: Computer Vision
  • Code discipline: R-03
  • Subject area:

Short Description

This course covers the following concepts: Computer vision techniques; Classical and deep learning models.

Prerequisites

Prerequisite subjects

Prerequisite topics

Course Topics

Course Sections and Topics
Section Topics within the section
Image Acquisition and Basic Image Processing
  1. Computer vision in action
  2. The Human Vision System
  3. Optical Illusions
  4. Sampling and Quantization
  5. Image Representation
  6. Colour Spaces
Image Filtering and Binary Vision
  1. Image noise
  2. Convolutions and kernels
  3. Smoothing and blurring
  4. Thresholding and histograms
  5. Morphological operations
  6. Gradients and Edge detection
Feature Extractors and Descriptors
  1. Histogram of Gradients (HoG)
  2. Scale-invariant feature transform (SIFT)
  3. Harris corner detector
  4. Template matching
  5. Bag of visual words
  6. Face Detection and Recognition (Viola Johns)
Deep learning models for computer vision
  1. You Only Look Once: Unified, Real-Time Object Detection (YOLO)
  2. Generative Adversarial Networks (GAN)
  3. Fully Convolutional Networks (FCN) for semantic segmentation
  4. Multi Domain Network (MDNet) for object tracking
  5. Generic Object Tracking Using Regression Networks (GOTURN) for object tracking

Intended Learning Outcomes (ILOs)

What is the main purpose of this course?

This course provides an intensive treatment of a cross-section of the key elements of computer vision, with an emphasis on implementing them in modern programming environments, and using them to solve real-world problems. The course will begin with the fundamentals of image processing and image filtering, but will quickly build to cover more advanced topics, including image segmentation, object detection and recognition, face detection, content-based image retrieval, artificial neural networks, convolutional neural networks, generative adversarial networks and much more. A key focus of the course is on providing students with not only theory but also hands-on practice of building their computer vision applications.

ILOs defined at three levels

Level 1: What concepts should a student know/remember/explain?

By the end of the course, the students should be able to ...

  • Robots visual perception strategies
  • Significant exposure to real-world implementations
  • To develop research interest in the theory and application of computer vision

Level 2: What basic practical skills should a student be able to perform?

By the end of the course, the students should be able to ...

  • Suitability of different computer vision models in different scenarios
  • Ability to choose the right model for the given task

Level 3: What complex comprehensive skills should a student be able to apply in real-life scenarios?

By the end of the course, the students should be able to ...

  • Hands on experience to implement different models to know inside behavior
  • Sufficient exposure to train and deploy model for the given task
  • Fine tune the deployed model in the real-world settings

Grading

Course grading range

Grade Range Description of performance
A. Excellent 90-100 -
B. Good 75-89 -
C. Satisfactory 60-74 -
D. Poor 0-59 -

Course activities and grading breakdown

Activity Type Percentage of the overall course grade
Labs/seminar classes 20
Interim performance assessment 50
Exams 30

Recommendations for students on how to succeed in the course

Resources, literature and reference materials

Open access resources

  • Handouts supplied by the instructor
  • Materials from the interment and research papers shared by instructor

Closed access resources

Software and tools used within the course

Teaching Methodology: Methods, techniques, & activities

Activities and Teaching Methods

Activities within each section
Learning Activities Section 1 Section 2 Section 3 Section 4
Development of individual parts of software product code 1 1 1 1
Homework and group projects 1 1 1 1
Midterm evaluation 1 1 1 1
Testing (written or computer based) 1 1 1 1
Discussions 1 1 1 1

Formative Assessment and Course Activities

Ongoing performance assessment

Section 1

Activity Type Content Is Graded?
Question What are the color spaces and where it’s used? 1
Question What are the primary and secondary colors? 1
Question How image is formed into computers? 1
Question How you will convert the RGB to grayscale images 1
Question Loading and plotting the images in python environment 0
Question Convertion of different color spaces 0
Question How you find the skin in the images based on the color space models 0
Question how to find red eye dot in face using color space models 0

Section 2

Activity Type Content Is Graded?
Question What are the challenges to perform histogram task? 1
Question Apply convolutional filter to calculate the response. 1
Question What kind of parameters are required to apply different image filters? 1
Question How you will compute the gradients of the image and its benefits? 1
Question Implement Otsu Method 0
Question Implement Sobel, Preweitt filters 0
Question Implement Canny edge detector 0
Question Perform analysis over the different filtering on the given images 0

Section 3

Activity Type Content Is Graded?
Question How feature extractor works over the given image? 1
Question What is the difference between the feature extraction and descriptors? 1
Question Explain the examples of descriptors and feature extractors. 1
Question Write down the pros and cons of SIFT, HOG and Harris. 1
Question Implement template matching algorithm 0
Question Implement histogram of gradient using CV2 library 0
Question Implement of SIFT for the given task 0
Question Implement Harris corner detection 0
Question Analysis of different extractors for the given task 0

Section 4

Activity Type Content Is Graded?
Question How classification task is different from detection task? 1
Question Explain the transfer learning mechanism for object detection task. 1
Question How many types of model exist for object tracking in videos. 1
Question Write down the pros and cons of YOLO, FCN and MDNet. 1
Question Implement YOLO using transfer learning mechanism 0
Question Implement GAN for MNIST dataset 0
Question Implement FCN and GOTURN 0
Question Analysis of different models for the given task 0

Final assessment

Section 1

  1. How you can distinguish different color spaces?
  2. Explain and provide the reason for the blind spot creation in human eye.
  3. In what scenarios computer vision is better than human vision?
  4. Write down different robotic application areas where computer vision is applied successfully.

Section 2

  1. Calculate the kernels for the given images
  2. Explain the difference between different filters
  3. What is image noise and how it contributes to make the computer vision task difficult?
  4. Apply different combination of the filters to achieve the required output of the given image.

Section 3

  1. How you distinguish different feature extractors and descriptors?
  2. What are the possible methods to detect the corners?
  3. How corners are useful to help the robotic vision task?
  4. How you will patch the different images to construct the map of the location?

Section 4

  1. What are the loss functions used in YOLO?
  2. What are the learnable parameters of FCN for semantic segmentation?
  3. How semantic segmentation is different from instance segmentation?
  4. Write the application areas for object tracking in robotics.

The retake exam

Section 1

Section 2

Section 3

Section 4

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